Predictive model for water absorption in sublayers using a Joint Distribution Adaption based XGBoost transfer learning method

Abstract

Calculating the water absorption in sublayers based on the on-site monitored water injection profile is the most accurate way to reflect the actual amount of water absorbed by subalyers. However, access to this profile data is scarce and limited due to its high cost of testing. As a result, some well blocks have tested some discontinuous profiles, but other well blocks do not have any profiles. Traditional machine learning can be applied to construct an intelligent surrogate model by training with historical injection profile. This well-trained model can be only used to make a good prediction for well blocks which contribute profile data, not applicable to well blocks without injection profile. In this study, a Joint Distribution Adaption based Extreme Gradient boosting transfer learning approach is presented to predict water absorption of sublayers in water injection well which do not have any injection profile. A handful of observations are obtained from source well block which has tested sufficient injection profiles. Joint Distribution Adaption is first conducted to transfer knowledge from source well block to the target well block which have no injection profile. The transferred dataset with new feature representation is used to constitute the training dataset for target well block. This transferred training dataset then can be feed into the Extreme Gradient Boosting model to construct a water absorption predictive model. The well-trained model can be applied to predicting water absorption of sublayers in injectors which do not have any water injection profile. The proposed approach is demonstrated by applying to two well blocks from SL oilfield, China. Demonstrated results imply that the proposed transfer learning method can be used to dividing water absorption of subalyers in injectors which have no water injection profile data.